Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/robium-ai/robium/1.2.1npx skills add robium-ai/robium --skill 1.2.1git clone --depth 1 https://github.com/robium-ai/robiumWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/robium-ai/robium/1.2.1)<a href="https://agentmods.dev/skills/robium-ai/robium/1.2.1"><img src="https://agentmods.dev/badge/skills/robium-ai/robium/1.2.1.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00153 | $0.02782 |
| Opus 5 | $0.00077 | $0.01391 |
| Sonnet 5 | $0.00031 | $0.00556 |
| Haiku 4.5 | $0.00015 | $0.00278 |
Grade A, and why
data scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 200 lines — stays where its author put it; the contents beside it link to each section on GitHub.
data
The data-sourcing umbrella for robium. Before any policy gets trained, something
has to decide where the training data comes from — an existing hub dataset, data
generated in simulation, or demonstrations collected via teleop on a real robot —
and how it will be stored and versioned once it exists. This skill owns that
selection and the cross-cutting sourcing rules; it does not own hub mechanics
(huggingface), the LeRobotDataset format (lerobot), or the mechanics of
generating synthetic data inside a simulator (isaac-sim, gazebo). It also
does not own training itself — that is lerobot and isaac-lab's territory.
When to use this skill
- Starting any robot-learning task and the data source isn't decided yet — this
is a required early step for the manipulation vertical, the same way
environmentsis a required early step for reproducibility. - The trigger phrases in the description: 'where do we get data', 'training data for the robot', 'dataset for manipulation', 'generate data in sim', 'collect demonstrations'.
- Planning storage format, episode structure, or dataset versioning before a collection or generation effort starts, not after.
- Cross-references — go to the sibling skill instead when the question is:
- Actually pulling, pushing, or browsing a dataset on the Hub →
huggingface. This skill decides which dataset or source strategy to use; it does not own hub auth or transfer mechanics. - The LeRobotDataset directory/Parquet+MP4 shape, recording CLI, or dataset
editing tools →
lerobot. This skill decides whether to record real demonstrations at all;lerobotowns how a recording actually happens. - The mechanics of generating synthetic data inside a simulator (Replicator,
domain randomization, writers) →
isaac-simorgazebo. This skill decides whether sim-generated data is the right call for a task. - Training a policy on the data once sourced →
lerobot(orisaac-labfor the NVIDIA RL stack). - The whole-stack decision this feeds into →
architect(routes here). - Sourcing test data — worlds, models, sample datasets, fixtures, and
goldens for smoke/regression tests →
test-assets. This skill owns data that trains policies;test-assetsowns data that tests apps.
- Actually pulling, pushing, or browsing a dataset on the Hub →
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 200 lines · 153 tokens per session scan A 49aec3b1608d
data is a skill published in the GitHub repository robium-ai/robium (9 stars, last pushed 7d ago), licensed MIT. It adds 153 tokens to every session and 2,782 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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